renamed file names

app.py

@@ -1,69 +1,58 @@
-# import os
-# import requests
-# from transformers import pipeline
-# import gradio as gr
-
-# # Use the token from environment variable (secret)
-# token = os.environ.get("OPENAI_API_KEY")
-# # generator = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", token=token)
-
-# # API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
-# API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-rw-1b"
-# headers = {"Authorization": f"Bearer {token}"}
-
-# # generator = pipeline("text-generation", model="tiiuae/falcon-rw-1b")
-
-# def query(payload):
-#     response = requests.post(API_URL, headers=headers, json=payload)
-#     return response.json()
-
-# def clean_topic(topic):
-#     topic = topic.lower()
-#     if "write a blog on" in topic:
-#         topic = topic.replace("write a blog on", "").strip()
-#     elif "write a blog about" in topic:
-#         topic = topic.replace("write a blog about", "").strip()
-#     return topic.capitalize()
-
-# # def generate_blog(topic):
-# #     topic = clean_topic(topic)
-# #     prompt = f"""
-# #     Write a detailed and engaging blog post about "{topic}".
-# #     Include an introduction, 2–3 subheadings with paragraphs, and a conclusion.
-# #     Make it informative and conversational.
-# #     """
-# #     # output = query({"inputs": prompt})
-# #     # return output[0]["generated_text"]
-# #     # result = generator(prompt, max_length=700, do_sample=True, temperature=0.7, top_p=0.9)
-# #     # result = generator(prompt, max_length=300, do_sample=True, temperature=0.7, top_p=0.9)
-
-# #     # return result[0]['generated_text']
-
-# #     output = query({"inputs": prompt})
-# #     return output[0]["generated_text"]
-
-# def generate_blog(topic):
-#     topic = clean_topic(topic)
-#     prompt = f"""
-#     Write a detailed and engaging blog post about "{topic}".
-#     Include an introduction, 2–3 subheadings with paragraphs, and a conclusion.
-#     Make it informative and conversational.
-#     """
-#     output = query({"inputs": prompt})
-#     print("API Response:", output)  # <-- Add this for debugging
-
-#     if isinstance(output, list) and "generated_text" in output[0]:
-#         return output[0]["generated_text"]
-#     elif "error" in output:
-#         return f"Error from model: {output['error']}"
-#     else:
-#         return "Failed to generate blog. Please try again."
-
-
-# gr.Interface(
-#     fn=generate_blog,
-#     inputs="text",
-#     outputs="text",
-#     title="AI Blog Writer"
-# ).launch(share=True)
-
+import os
+import requests
+import gradio as gr
+
+# Get token from environment variable (must be set in Hugging Face Secrets for Spaces)
+HF_TOKEN = os.environ.get("OPENAI_API_KEY")
+API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def clean_topic(topic):
+    topic = topic.lower()
+    if "write a blog on" in topic:
+        topic = topic.replace("write a blog on", "").strip()
+    elif "write a blog about" in topic:
+        topic = topic.replace("write a blog about", "").strip()
+    return topic.capitalize()
+
+def generate_blog(topic):
+    topic = clean_topic(topic)
+    prompt = f"""### Instruction:
+Write a complete, friendly, and engaging blog post about "{topic}". 
+Structure the blog with:
+- A title
+- An introduction
+- 2–3 subheadings with paragraphs
+- A conclusion
+
+Use markdown formatting with ## for subheadings. Keep the tone conversational.
+
+### Response:
+"""
+
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": 700,
+            "do_sample": True,
+            "temperature": 0.7,
+            "top_p": 0.9,
+        }
+    }
+
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        response.raise_for_status()
+        generated_text = response.json()[0]['generated_text']
+        return generated_text.split("### Response:")[-1].strip()
+    except Exception as e:
+        return f"❌ Failed to generate blog: {str(e)}"
+
+# Gradio Interface
+gr.Interface(
+    fn=generate_blog,
+    inputs=gr.Textbox(label="Blog Topic"),
+    outputs=gr.Textbox(label="Generated Blog"),
+    title="📝 AI Blog Writer",
+    description="Enter a topic and get a well-written blog post.",
+).launch(share=True)

app1.py

@@ -1,58 +1,69 @@
-import os
-import requests
-import gradio as gr
-
-# Get token from environment variable (must be set in Hugging Face Secrets for Spaces)
-HF_TOKEN = os.environ.get("OPENAI_API_KEY")
-API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-def clean_topic(topic):
-    topic = topic.lower()
-    if "write a blog on" in topic:
-        topic = topic.replace("write a blog on", "").strip()
-    elif "write a blog about" in topic:
-        topic = topic.replace("write a blog about", "").strip()
-    return topic.capitalize()
-
-def generate_blog(topic):
-    topic = clean_topic(topic)
-    prompt = f"""### Instruction:
-Write a complete, friendly, and engaging blog post about "{topic}". 
-Structure the blog with:
-- A title
-- An introduction
-- 2–3 subheadings with paragraphs
-- A conclusion
-
-Use markdown formatting with ## for subheadings. Keep the tone conversational.
-
-### Response:
-"""
-
-    payload = {
-        "inputs": prompt,
-        "parameters": {
-            "max_new_tokens": 700,
-            "do_sample": True,
-            "temperature": 0.7,
-            "top_p": 0.9,
-        }
-    }
-
-    try:
-        response = requests.post(API_URL, headers=headers, json=payload)
-        response.raise_for_status()
-        generated_text = response.json()[0]['generated_text']
-        return generated_text.split("### Response:")[-1].strip()
-    except Exception as e:
-        return f"❌ Failed to generate blog: {str(e)}"
-
-# Gradio Interface
-gr.Interface(
-    fn=generate_blog,
-    inputs=gr.Textbox(label="Blog Topic"),
-    outputs=gr.Textbox(label="Generated Blog"),
-    title="📝 AI Blog Writer",
-    description="Enter a topic and get a well-written blog post.",
-).launch(share=True)
+# import os
+# import requests
+# from transformers import pipeline
+# import gradio as gr
+
+# # Use the token from environment variable (secret)
+# token = os.environ.get("OPENAI_API_KEY")
+# # generator = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", token=token)
+
+# # API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
+# API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-rw-1b"
+# headers = {"Authorization": f"Bearer {token}"}
+
+# # generator = pipeline("text-generation", model="tiiuae/falcon-rw-1b")
+
+# def query(payload):
+#     response = requests.post(API_URL, headers=headers, json=payload)
+#     return response.json()
+
+# def clean_topic(topic):
+#     topic = topic.lower()
+#     if "write a blog on" in topic:
+#         topic = topic.replace("write a blog on", "").strip()
+#     elif "write a blog about" in topic:
+#         topic = topic.replace("write a blog about", "").strip()
+#     return topic.capitalize()
+
+# # def generate_blog(topic):
+# #     topic = clean_topic(topic)
+# #     prompt = f"""
+# #     Write a detailed and engaging blog post about "{topic}".
+# #     Include an introduction, 2–3 subheadings with paragraphs, and a conclusion.
+# #     Make it informative and conversational.
+# #     """
+# #     # output = query({"inputs": prompt})
+# #     # return output[0]["generated_text"]
+# #     # result = generator(prompt, max_length=700, do_sample=True, temperature=0.7, top_p=0.9)
+# #     # result = generator(prompt, max_length=300, do_sample=True, temperature=0.7, top_p=0.9)
+
+# #     # return result[0]['generated_text']
+
+# #     output = query({"inputs": prompt})
+# #     return output[0]["generated_text"]
+
+# def generate_blog(topic):
+#     topic = clean_topic(topic)
+#     prompt = f"""
+#     Write a detailed and engaging blog post about "{topic}".
+#     Include an introduction, 2–3 subheadings with paragraphs, and a conclusion.
+#     Make it informative and conversational.
+#     """
+#     output = query({"inputs": prompt})
+#     print("API Response:", output)  # <-- Add this for debugging
+
+#     if isinstance(output, list) and "generated_text" in output[0]:
+#         return output[0]["generated_text"]
+#     elif "error" in output:
+#         return f"Error from model: {output['error']}"
+#     else:
+#         return "Failed to generate blog. Please try again."
+
+
+# gr.Interface(
+#     fn=generate_blog,
+#     inputs="text",
+#     outputs="text",
+#     title="AI Blog Writer"
+# ).launch(share=True)
+