Update app.py

app.py

@@ -26,7 +26,7 @@ def query_zephyr(prompt):
     else:
         raise ValueError("Unexpected response format from Zephyr API")
 
-def generate_image_from_prompt(prompt, guidance_scale=7.5, width=1024, height=768, num_inference_steps=30):
+def generate_image_from_prompt(prompt, negative_prompt="", guidance_scale=7.5, width=1024, height=768, num_inference_steps=30):
     headers = {"Authorization": f"Bearer {SD_API_TOKEN}"}
     payload = {
         "inputs": prompt,
@@ -37,28 +37,47 @@ def generate_image_from_prompt(prompt, guidance_scale=7.5, width=1024, height=76
             "num_inference_steps": num_inference_steps,
         },
     }
+    if negative_prompt:  # Add negative_prompt to the payload if provided
+        payload["parameters"]["negative_prompt"] = negative_prompt
+
     response = requests.post(SD_API_URL, headers=headers, json=payload)
     image_bytes = response.content
     image = Image.open(io.BytesIO(image_bytes))
     return image
 
-def generate_image_and_show_prompt(linkedin_text):
-    # Generate a prompt from the LinkedIn text using Zephyr
-    generated_prompt = query_zephyr(linkedin_text)
+def generate_image_from_linkedin_text(linkedin_text, negative_prompt, guidance_scale, width, height, num_inference_steps):
+    zephyr_response = query_zephyr(linkedin_text)
+    
+    if zephyr_response and isinstance(zephyr_response, list):
+        generated_prompt = zephyr_response[0].get("generated_text", "")
+    else:
+        raise ValueError("Unexpected response format from Zephyr model.")
+    
+    print("Generated Prompt:", generated_prompt)  # For backend logs
     
-    # Use the generated prompt to create an image with Stable Diffusion
     if generated_prompt:
-        image = generate_image_from_prompt(generated_prompt)
+        image = generate_image_from_prompt(generated_prompt, negative_prompt, guidance_scale, width, height, num_inference_steps)
         return image, generated_prompt
     else:
         raise ValueError("Failed to generate a prompt from the LinkedIn text.")
 
+# Update Gradio interface with additional parameters
 iface = gr.Interface(
-    fn=generate_image_and_show_prompt,
-    inputs=[gr.Textbox(label="LinkedIn Message", placeholder="Enter LinkedIn message here...")],
-    outputs=[gr.Image(type="pil"), gr.Textbox(label="Generated Prompt")],
+    fn=generate_image_from_linkedin_text,
+    inputs=[
+        gr.Textbox(label="LinkedIn Message", placeholder="Enter LinkedIn message here..."),
+        gr.Textbox(label="Negative Prompt", placeholder="Enter a negative prompt here (optional)..."),
+        gr.Slider(label="Guidance Scale", minimum=1, maximum=20, step=0.1, value=7.5),
+        gr.Slider(label="Width", minimum=768, maximum=1024, step=1, value=1024),
+        gr.Slider(label="Height", minimum=768, maximum=1024, step=1, value=768),
+        gr.Slider(label="Number of Inference Steps", minimum=20, maximum=50, step=1, value=30)
+    ],
+    outputs=[
+        gr.Image(type="pil"),
+        gr.Textbox(label="Generated Prompt", readonly=True)
+    ],
     title="Generate Images from LinkedIn Messages",
-    description="Enter a LinkedIn message to generate a creative prompt with Zephyr, which is then used to generate an image with Stable Diffusion. The generated prompt is also displayed."
+    description="Enter a LinkedIn message to generate a creative prompt with Zephyr, which is then used to generate an image with Stable Diffusion. Additional parameters allow for fine-tuning of the generated image."
 )
 
 iface.launch()