Create app.py

app.py

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+# app.py
+import os
+import io
+import tempfile
+import streamlit as st
+from huggingface_hub import InferenceClient
+import pdfplumber
+from PIL import Image
+import base64
+
+# ---------- Configuration ----------
+HF_TOKEN = os.environ.get("HF_TOKEN")  # required
+GROQ_KEY = os.environ.get("GROQ_API_KEY")  # optional: if you want to call Groq directly
+USE_GROQ_PROVIDER = True  # set False to route to default HF provider
+
+# model IDs (change if you prefer other models)
+LLAMA_MODEL = "Groq/Llama-3-Groq-8B-Tool-Use"        # Groq Llama model on HF
+TTS_MODEL = "espnet/kan-bayashi_ljspeech_vits"       # a HF-hosted TTS model example
+SDXL_MODEL = "stabilityai/stable-diffusion-xl-base-1.0"  # SDXL base model
+
+# create Inference client (route via HF token by default)
+if USE_GROQ_PROVIDER:
+    client = InferenceClient(provider="groq", api_key=HF_TOKEN)
+else:
+    client = InferenceClient(api_key=HF_TOKEN)
+
+# ---------- Helpers ----------
+def pdf_to_text(uploaded_file) -> str:
+    text_chunks = []
+    with pdfplumber.open(uploaded_file) as pdf:
+        for page in pdf.pages:
+            ptext = page.extract_text()
+            if ptext:
+                text_chunks.append(ptext)
+    return "\n\n".join(text_chunks)
+
+def llama_summarize(text, max_tokens=512):
+    prompt = [
+        {"role": "system", "content": "You are a concise summarizer. Produce a clear summary in bullet points."},
+        {"role": "user", "content": f"Summarize the following document in <= 8 bullet points. Keep it short:\n\n{text}"}
+    ]
+    # Use chat completion endpoint style
+    resp = client.chat.completions.create(model=LLAMA_MODEL, messages=prompt)
+    try:
+        summary = resp.choices[0].message["content"]
+    except Exception:
+        # fallback: try text generation field
+        summary = resp.choices[0].text if hasattr(resp.choices[0], "text") else str(resp)
+    return summary
+
+def llama_chat(chat_history, user_question):
+    messages = chat_history + [{"role":"user","content":user_question}]
+    resp = client.chat.completions.create(model=LLAMA_MODEL, messages=messages)
+    return resp.choices[0].message["content"]
+
+def tts_synthesize(text) -> bytes:
+    # InferenceClient offers text->audio utilities. This returns raw audio bytes (wav).
+    audio_bytes = client.text_to_speech(model=TTS_MODEL, inputs=text)
+    return audio_bytes
+
+def generate_image(prompt_text) -> Image.Image:
+    img_bytes = client.text_to_image(prompt_text, model=SDXL_MODEL)
+    return Image.open(io.BytesIO(img_bytes))
+
+def audio_download_button(wav_bytes, filename="summary.wav"):
+    b64 = base64.b64encode(wav_bytes).decode()
+    href = f'<a href="data:audio/wav;base64,{b64}" download="{filename}">Download audio (WAV)</a>'
+    st.markdown(href, unsafe_allow_html=True)
+
+# ---------- Streamlit UI ----------
+st.set_page_config(page_title="PDFGPT (Groq + HF)", layout="wide")
+st.title("PDF → Summary + Speech + Chat + Diagram (Groq + HF)")
+
+uploaded = st.file_uploader("Upload PDF", type=["pdf"])
+if uploaded:
+    with st.spinner("Extracting text from PDF..."):
+        text = pdf_to_text(uploaded)
+    st.subheader("Extracted text (preview)")
+    st.text_area("Document text", value=text[:1000], height=200)
+
+    if st.button("Create summary (Groq Llama)"):
+        with st.spinner("Summarizing with Groq Llama..."):
+            summary = llama_summarize(text)
+        st.subheader("Summary")
+        st.write(summary)
+        st.session_state["summary"] = summary
+
+    if "summary" in st.session_state:
+        summary = st.session_state["summary"]
+        if st.button("Synthesize audio from summary (TTS)"):
+            with st.spinner("Creating audio..."):
+                try:
+                    audio = tts_synthesize(summary)
+                    st.audio(audio)
+                    audio_download_button(audio)
+                except Exception as e:
+                    st.error(f"TTS failed: {e}")
+
+    st.markdown("---")
+    st.subheader("Chat with your PDF (ask questions about document)")
+    if "chat_history" not in st.session_state:
+        # start with system + doc context (shortened)
+        doc_context = (text[:4000] + "...") if len(text) > 4000 else text
+        st.session_state["chat_history"] = [
+            {"role":"system","content":"You are a helpful assistant that answers questions based on the provided document."},
+            {"role":"user","content": f"Document context:\n{doc_context}"}
+        ]
+
+    user_q = st.text_input("Ask a question about the PDF")
+    if st.button("Ask") and user_q:
+        with st.spinner("Getting answer from Groq Llama..."):
+            answer = llama_chat(st.session_state["chat_history"], user_q)
+            st.session_state.setdefault("convo", []).append(("You", user_q))
+            st.session_state.setdefault("convo", []).append(("Assistant", answer))
+            # append to history for next calls
+            st.session_state["chat_history"].append({"role":"user","content":user_q})
+            st.session_state["chat_history"].append({"role":"assistant","content":answer})
+            st.write(answer)
+
+    st.markdown("---")
+    st.subheader("Generate a diagram from your question (SDXL)")
+    diagram_prompt = st.text_input("Describe the diagram or scene to generate")
+    if st.button("Generate diagram") and diagram_prompt:
+        with st.spinner("Generating image (SDXL)..."):
+            try:
+                img = generate_image(diagram_prompt)
+                st.image(img, use_column_width=True)
+                # allow download
+                buf = io.BytesIO()
+                img.save(buf, format="PNG")
+                st.download_button("Download diagram (PNG)", data=buf.getvalue(), file_name="diagram.png", mime="image/png")
+            except Exception as e:
+                st.error(f"Image generation failed: {e}")
+
+st.sidebar.title("Settings")
+st.sidebar.write("Models in use:")
+st.sidebar.write(f"LLM: {LLAMA_MODEL}")
+st.sidebar.write(f"TTS: {TTS_MODEL}")
+st.sidebar.write(f"Image: {SDXL_MODEL}")
+
+st.sidebar.markdown("**Notes**\n- Set HF_TOKEN in Space secrets or environment before starting.\n- To route directly to Groq with your Groq API key, set `GROQ_API_KEY` and change the client init accordingly.")