Create app.py

app.py

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+# app.py
+import os
+from google import genai
+import gradio as gr
+import PyPDF2
+import numpy as np
+
+# Try importing DSPy for chain-of-thought reasoning
+try:
+    import dspy
+    HAS_DSPY = True
+except ImportError:
+    HAS_DSPY = False
+
+#############################################
+# Load Gemini API key from environment variable
+#############################################
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+if not GEMINI_API_KEY:
+    raise ValueError("Please set the GEMINI_API_KEY environment variable.")
+
+# Initialize the Gemini API client with the secret key
+client = genai.Client(api_key=GEMINI_API_KEY)
+
+#############################################
+# Custom DSPy Prompt Signature Function
+#############################################
+def custom_dspy_prompt(text, mode="summarization"):
+    """
+    Returns a custom chain-of-thought prompt signature for DSPy.
+    Modes:
+      - "summarization": for summarizing a text chunk.
+      - "overall": for combining chunk summaries.
+    """
+    if mode == "summarization":
+        return (f"EffectiveDSPyCOT: Please provide a detailed, robust, and token-expansive summary using chain-of-thought reasoning. "
+                f"Preserve context and key details. Text:\n\n{text}")
+    elif mode == "overall":
+        return (f"EffectiveDSPyCOT: Combine the following chunk summaries into an overall comprehensive summary. "
+                f"Expand on details and maintain context with chain-of-thought reasoning. Summaries:\n\n{text}")
+    else:
+        return text
+
+#############################################
+# Fallback Using Gemini's generate_content Method
+#############################################
+def fallback_predict(prompt, system_msg="You are a helpful assistant."):
+    """
+    Uses the Gemini API (generate_content method) to generate content.
+    """
+    try:
+        full_prompt = f"{system_msg}\n\n{prompt}"
+        response = client.models.generate_content(
+            model="gemini-2.0-flash",  # Adjust model name as needed.
+            contents=full_prompt
+        )
+        return response.text
+    except Exception as e:
+        return f"[Gemini fallback error]: {str(e)}"
+
+#############################################
+# PDF Extraction and Improved Chunking
+#############################################
+def extract_text_from_pdf(pdf_path):
+    """
+    Extract text from all pages of a PDF file.
+    """
+    text = ""
+    with open(pdf_path, "rb") as f:
+        pdf_reader = PyPDF2.PdfReader(f)
+        for page in pdf_reader.pages:
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text + "\n"
+    return text
+
+def chunk_text(text, chunk_size=2000, overlap=300):
+    """
+    Split the text into overlapping chunks.
+    Larger chunk size and overlap help maintain context and expand token capacity.
+    """
+    words = text.split()
+    chunks = []
+    start = 0
+    while start < len(words):
+        end = min(start + chunk_size, len(words))
+        chunk = " ".join(words[start:end])
+        chunks.append(chunk)
+        start += chunk_size - overlap  # Advance with overlap
+    return chunks
+
+#############################################
+# Summarizing a Single Chunk with Custom DSPy / Gemini
+#############################################
+def summarize_chunk(chunk):
+    """
+    Summarize a text chunk using a custom DSPy chain-of-thought prompt.
+    Falls back to Gemini if DSPy is not available or fails.
+    """
+    prompt = custom_dspy_prompt(chunk, mode="summarization")
+    if HAS_DSPY:
+        try:
+            summary = dspy.predict(prompt)
+        except Exception as e:
+            summary = fallback_predict(prompt, system_msg="You are a helpful summarizer.")
+    else:
+        summary = fallback_predict(prompt, system_msg="You are a helpful summarizer.")
+    return summary
+
+#############################################
+# Summarizing the Entire PDF
+#############################################
+def summarize_document(pdf_path):
+    """
+    Extract text from PDF, split it into overlapping chunks, summarize each chunk,
+    and then combine the chunk summaries into an overall document summary.
+    """
+    text = extract_text_from_pdf(pdf_path)
+    chunks = chunk_text(text)
+    
+    summaries = []
+    for chunk in chunks:
+        summary = summarize_chunk(chunk)
+        summaries.append(summary)
+    
+    overall_prompt = custom_dspy_prompt("\n\n".join(summaries), mode="overall")
+    if HAS_DSPY:
+        try:
+            overall_summary = dspy.predict(overall_prompt)
+        except Exception as e:
+            overall_summary = fallback_predict(overall_prompt, system_msg="You are a helpful assistant that summarizes documents.")
+    else:
+        overall_summary = fallback_predict(overall_prompt, system_msg="You are a helpful assistant that summarizes documents.")
+    
+    return overall_summary, summaries
+
+#############################################
+# Enhanced Gradio Interface with Better UI Aesthetics (Summarization Only)
+#############################################
+custom_css = """
+<style>
+    body { background-color: #f4f7f9; }
+    .gradio-container { font-family: 'Arial', sans-serif; }
+    h1, h2, h3 { color: #333333; }
+    .tab-header { background-color: #ffffff; border-bottom: 2px solid #e0e0e0; }
+    .gr-button { background-color: #4CAF50; color: white; }
+    .gr-textbox { background-color: #ffffff; }
+</style>
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("## PDF Summarization Interface with Gemini API\n"
+                "Upload a PDF document to get a robust, detailed summary using a custom DSPy chain-of-thought prompt.\n")
+    
+    with gr.Row():
+        pdf_input_sum = gr.File(label="Upload PDF for Summarization", file_types=['.pdf'])
+        summarize_button = gr.Button("Summarize Document")
+    overall_summary_output = gr.Textbox(label="Overall Document Summary", lines=8)
+    chunk_summaries_output = gr.Textbox(label="Chunk Summaries", lines=10)
+    
+    def process_and_summarize(pdf_file):
+        if pdf_file is None:
+            return "No file uploaded.", "No file uploaded."
+        file_path = pdf_file.name
+        overall, chunks = summarize_document(file_path)
+        return overall, "\n\n".join(chunks)
+    
+    summarize_button.click(
+        fn=process_and_summarize,
+        inputs=pdf_input_sum,
+        outputs=[overall_summary_output, chunk_summaries_output]
+    )
+    
+demo.launch()